The invention relates to a coronary
artery sequence
blood vessel segmentation method based on space-time discriminative
feature learning, which is used for carrying out
blood vessel segmentation
processing on a cardiac coronary
artery angiography sequence image, and includes
processing a current frame of image and several adjacent frames of images based on a pre-trained improved Unet
network model, and obtaining
blood vessel segmentation result of current frame image, wherein the improved Unet
network model comprises a coding part, a jump connection layer and a decoding part, the coding part adopts a 3D
convolution layer to perform time-space
feature extraction, the decoding part is provided with a channel attention module, and the jump connection layer aggregates features extracted by thecoding part, thus obtaining an aggregation feature map and transmitting the aggregation feature map to the decoding part. Compared with the prior art, the cardiac coronary
artery blood
vessel segmentation method introduces the spatial-temporal features to perform cardiac coronary artery blood
vessel segmentation, reduces the interference of
time domain noise, emphasizes the blood vessel features,alleviates the problem of
class imbalance in blood
vessel segmentation, and has higher blood vessel segmentation accuracy.